On the utility of a well-mixed model for predicting disease transmission on an urban bus

Abstract

The transport of virus-laden aerosols from a host to a susceptible person is governed by complex turbulent airflow and physics related to breathing, coughing and sneezing, mechanical and passive ventilation, thermal buoyancy effects, surface deposition, masks, and air filtration. In this paper, we study the infection risk via airborne transmission on an urban bus using unsteady Reynolds-averaged Navier–Stokes equations and a passive-scalar model of the virus-laden aerosol concentration. Results from these simulations are directly compared to the widely used well-mixed model and show significant differences in the concentration field and number of inhaled particles. Specifically, in the limit of low mechanical ventilation rates, the well-mixed model will overpredict the concentration far from the infected passenger and substantially underpredict the concentration near the infected passenger. The results reported herein also apply to other enclosed spaces.

COVID-19 transmission and control in land public transport: A literature review

“…However, the high ACH also may promote the spread of the virus among the passengers and increase energy consumption. The time scale of transit from an infector to other passengers is less than a minute with the HVAC system under its maximum settings [60] . When the ventilation system is under a typical setting (75% force cooling), the air cannot be well mixed along the length of an intercity train carriage [44] .…”

Ultraviolet Germicidal Irradiation Development Method for Transportation Disinfection Modelling

“…The understanding was that the COVID-19 virus could transfer person to person by three progressively finer mechanisms: fomites, droplet ballistics and aerosolization. Other work was being done around the world at this time mapping the spread of the virus [13]. Fomites are inanimate objects with surfaces contaminated by a virus, leading to infection via contact.…”

Computational analysis of a new biomimetic active ventilation paradigm for indoor spaces

“…Computational models were implemented for different rooms to simulate natural ventilation via designed openings with and without human models (Detaranto, 2014; Etheridge, 2015; Gilani et al , 2016; Mousa et al , 2017), as well as the efficiency and air quality of air conditioning for different designs of air inlets and outlets (Hormigos-Jimenez et al , 2018; Nardecchia et al , 2022; Ning et al , 2016; Younsi et al , 2019). It should be noted that the efficiency of interior ventilation is subjected to the definitions and assumptions of the specific study or regulation (Bazant and Bush, 2021; Liddament, 1993; Sandberg, 1981; Zhang et al , 2021). In some studies, the association between ventilation and indoor air quality was explored by simulating an age-of-air concept or the distribution of CO 2 for different combinations of air supply and exhaust openings (Deng et al , 2018; Kwon et al , 2011; Ning et al , 2016).…”

“…In some studies, the association between ventilation and indoor air quality was explored by simulating an age-of-air concept or the distribution of CO 2 for different combinations of air supply and exhaust openings (Deng et al , 2018; Kwon et al , 2011; Ning et al , 2016). In other studies, the indoor transmission of airborne transport of pathogens and viruses like COVID-19 was simulated using methodologies of aerosol transport in different settings of various occupied indoors (Ho, 2021; Liu et al , 2022; Vuorinen et al , 2020; Yan et al , 2020; Yang et al , 2020; Zhang et al , 2021).…”